The present invention relates generally to a Universal Controller, for control of unknown systems via deconvolution; or more particularly to a logical network, which when interfaced with a mechanical system or an electromechanical system, will cause the output variable of the system to closely follow the output of a model system driven by the same command signal that the Universal Controller receives.
Model reference control algorithms are synthesized on the basis that the plant may be described by a transfer function with unknown coefficients. A basic assumption under which the stability of these algorithms is proven is that the order of the plant transfer function be known.
The uncertainty in the coefficients or parameter values of a transfer function of a given order may be referred to as structured uncertainty. Most practical systems have high frequency plant dynamics which cannot be described by transfer functions of a given order for any values of the parameters. These high frequency dynamics are called unmodeled or unstructured dynamics. Unmodeled dynamics affect the performance of model reference adaptive control systems in a peculiar way. Recent studies (C. E. Rohrs et al, "Robustness of Continuous-Time Adaptive Control Algorithms in the Presence of Unmodeled Dynamics" IEEE Transactions on Automatic Control Vol AC-30, pp 881-889, Sept. 1985) show that in the presence of unmodeled dynamics, a model reference adaptive control system becomes unstable if the reference input contains a high frequency sinusoid. Instability also occurs of there is a sinusoidal output disturbance at any frequency including d.c. The latter poses a serious problem because sinusoidal disturbances are common and the problem cannot be alleviated by adding a low-pass filter at the output (Rohrs et al ibid.). A remedy for this problem is to add low frequency excitation of sufficient magnitude at the input (Rohrs et al ibid. and K. J. Astrom, "A Commentary on the C. E. Rohrs et al Paper . . . " IEEE Transactions on Automatic Control, Vol AC-30, pp 889, Sept. 1985). A recent study (J. Krause et al. " Robustness Studies in Adaptive Control", Proceedings 22nd, IEEE Conference Decision Control, San Antonio, Tex., Dec. 14-16, 1983, pp 977-981) also deals with the frequency range and the amount of excitation required to stabilize the adaptive control system in the presence of unmodeled dynamics and output disturbances.
Many practical systems (for example, flight control systems) exist where persistent excitation in the input signal is undesirable. Thus, the problem with unmodeled dynamics reduces the appeal or even precludes the application of adaptive control to such systems.
R. E. Kalman ("Design of a Self-Optimizing Control System" Transactions of ASME, Vol 80, pp 468-478, Feb. 1958) mentioned the idea of modeling a plant in terms of an impulse response sequence in the design of a self-optimizing control system. However, Kalman did not succeed in developing an adaptive controller based on on-line identification of impulse-response sequence of an unknown system. In the work of Kalman, the idea of impulse-response sequence was abandoned, and his control algorithm computes the coefficients of the rational transfer function of the unknown plant via a modified least square filtering procedure, which requires lengthy and involved calculations. Kalman's machine requires a considerable amount of programmed computation. Furthermore, in Kalman's design, the order of the plant must be known. Each machine is built for plants that are lower than certain order. The computation grows exponentially with the order of the plant. Because of the computation time involved, the usefulness of the Kalman machine is limited to low order plants and low-sampling frequency digitalization.
Control designs based on approximate impulse-response models have been developed. See R. K. Mehra et al. "Basic Research in Digital Stochastic model Algorithmic Control" Technical Report AFWAL-TR-80-3125; and W. E. Larimore & S. Mahmood, "Basic Research on Adaptive model Algorithmic Control", Technical Report AFWAL-TR-85-3113 (available from NTIS as AD-A168 016), both from Flight Dynamics Laboratory, Wright-Patterson Air Force Base, Ohio. Model Algorithmic Control (MAC), described in the Larimore & Mahmood Report, assumes that the unit-impulse response sequence of the controlled system (the plant) be known. The control variable is then computed from the desired output and the unit-impulse response sequence. The Adaptive MAC requires that the unknown plant be operated open-loop and off-line initially for seven seconds to enable its unit-response be identified. The impulse-response model is then used in the digital controller and the control loop is closed. A (random) dither signal and a measurement noise of sufficient magnitude and variance are deliberately introduced and superimposed on the actual input and output to enable the repeated identification of the plant's impulse response model every seven seconds. Thus, the Adaptive MAC cannot help being contaminated by an artificially introduced dither signal at the input and measurement noise at the output and at the feedback.
Jones et al in U.S. Pat. No. 4,663,704 show a universal process control device which combines both control and data acquisition functions. Rake et al in U.S. Pat. No. 4,639,853 teach an adaptive controller of a continuous time process which includes prediction and parameter estimation. In column 1 the Rake et al patent speaks of establishing a process model by known parameter estimation methods. Courtiol in U.S. Pat. No. 4,795,799 discloses a process control arrangement employing the deviation between the output vectors of a reference model and of the process employed. The process in this patent follows the reference model without it being necessary to know the relationship between the input variables and the output variables. Mitsuoka in U.S. Pat. No. 4,437,045 discusses a servo control system in which a reference model 2 is in parallel with a final control element 1. Adaptive control is obtained based upon the difference between the outputs of the final control element and the reference model. Pfersch in U.S. Pat. No. 3,361,394 controls an aircraft in response to gain adjusted error signals. Sumano et al in U.S. Pat. No. 4,577,270 et al control a plant with a model constructed using a Kalman filter.